목록STUDY/ADP, 빅데이터분석기사 (17)

데이터 공부를 기록하는 공간

[clustering] Mall_Customers

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') df = pd.read_csv('./Mall_Customers/Mall_Customers.csv') print(df.shape) df.head(3) df = df.rename(columns = {"Annual Income (k$)": "income", "Spending Score (1-100)":"score", "Gender":"gender", "Age":"age"}) sns.pairplot(df, hue='gender') df.drop('Custome..
STUDY/ADP, 빅데이터분석기사 2021. 3. 21. 12:47
[classification] STAY or LEAVE

1. 데이터 전처리 import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns df = pd.read_csv("./mobile_cust_churn/mobile_cust_churn.csv") df.drop(columns=['Unnamed: 0','id'], axis=1, inplace=True) target = 'CHURN' features = df.columns.tolist()[:-1] numeric_features = df.select_dtypes(include=['int64']).columns.tolist() category_features= [] for col in features: if co..
STUDY/ADP, 빅데이터분석기사 2021. 3. 21. 11:07
[ARIMA] airplane

1. library import import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns plt.style.use('seaborn-whitegrid') from datetime import datetime import statsmodels.api as sm from statsmodels.graphics.tsaplots import plot_acf, plot_pacf from statsmodels.tsa.arima_model import ARIMA from statsmodels.tsa.statespace.sarimax import SARIMAX from matplotlib.pyplot import ..
STUDY/ADP, 빅데이터분석기사 2021. 3. 20. 13:16
[classification] titanic

import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns import warnings warnings.filterwarnings('ignore') train = pd.read_csv('./titanic/train.csv') test = pd.read_csv('./titanic/test.csv') 1. 데이터 전처리 # check null data train.isnull().sum() test.isnull().sum() # category, numeric feature seperation target = 'Survived' train[target].value_counts() features = tr..
STUDY/ADP, 빅데이터분석기사 2021. 3. 20. 10:51
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